Reading electronic memory utilizing relationships between cell state distributions

ABSTRACT

Providing distinction between overlapping state distributions of one or more multi cell memory devices is described herein. By way of example, a system can include a calculation component that can perform a mathematical operation on an identified, non-overlapped bit distribution and an overlapped bit distribution associated with the memory cell. Such mathematical operation can produce a resulting distribution that can facilitate identification by an analysis component of at least one overlapped bit distribution associated with cells of the one or more multi cell memory devices. Consequently, read errors associated with overlapped bits of a memory cell device can be mitigated.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of U.S. patent application Ser. No.11/957,309, filed on Dec. 14, 2007, entitled “READING ELECTRONIC MEMORYUTILIZING RELATIONSHIPS BETWEEN CELL STATE DISTRIBUTIONS”, the entiretyof which is incorporated herein by reference.

BACKGROUND

Memory devices have a wide variety of uses in modern electronics,including computers, cameras, voice recorders, cell phones, portablestorage drives, and similar devices. In addition, many types of memorydevices have been developed to affect such uses. Flash memory, forexample, is one type of electronic memory media that can store, eraseand restore data. Furthermore, flash memory, unlike some types ofelectronic memory, can retain stored data without continuous electricalpower. Flash memory has become a popular device for consumerelectronics, due in part to a combination of the high density and lowcost of erasable programmable read only memory (EPROM) and electricalerasability introduced with electronically erasable programmable readonly memory (EEPROM). In addition to combining these benefits, flashmemory is nonvolatile (e.g. flash memory can be removed from a powersource without losing stored data). Consequently, it has become a usefuland popular mechanism for storing, transporting, sharing and maintainingdata.

To further evolve technical capabilities associated with flash memorydevices, multiple storage cells have been implemented therewith.Multiple storage cells associated with a flash memory device cantypically increase a density and consequently a storage capacity of suchdevice. For example, a dual storage cell enables a single flash memorychip to store two data bits on a single chip. Some side effects canresult from multi cell devices, however, as a bit (e.g., represented bya quantized voltage or current level) stored in one cell can affect avoltage or current level, representing a particular bit, of an adjacentcell. In some situations electrical characteristics associated with twodifferent bits of a memory cell can overlap, making those bits difficultto distinguish. Such a condition can produce a memory read errorresulting from an inability to distinguish between two or more bitstates associated with a cell. To increase reliability and accuracyassociated with flash memory, read errors should be reduced oreliminated where possible. To facilitate cell read accuracy,semiconductor suppliers have developed mechanisms to distinguishpotentially overlapping cells of such multi-cell devices.

SUMMARY

The following presents a simplified summary of the innovation in orderto provide a basic understanding of some aspects described herein. Thissummary is not an extensive overview of the disclosed subject matter. Itis intended to neither identify key or critical elements of thedisclosed subject matter nor delineate the scope of the subjectinnovation. Its sole purpose is to present some concepts of thedisclosed subject matter in a simplified form as a prelude to the moredetailed description that is presented later.

The disclosed subject matter provides for differentiating betweenoverlapping memory cell bits in a multi-cell memory device. In accordwith aspects of the claimed subject matter, a bit state of a memory cellcan be differentiated from a second, overlapping bit state by performinga mathematical operation on an identified, non-overlapped bitdistribution and an overlapped bit distribution associated with thememory cell. Such mathematical operation can produce a resultingdistribution that can facilitate identification of at least oneoverlapped bit distribution associated with the memory cell.Consequently, read errors associated with overlapped bits of a memorycell device can be mitigated.

In accord with additional aspects of the claimed subject matter,overlapping bit state distributions associated with a plurality ofmulti-cell memory devices can be distinguished. A bit state associatedwith a non-overlapping bit can be uniquely identified. Subsequently, anoverlapped bit distribution having certain logical relationships withthe identified, non-overlapped distribution can be added or subtractedwith such distribution to yield a resulting distribution with smalldispersity. Such a resulting distribution can be used to identify astate of at least one overlapped bit state distribution, based on anexpected result of the addition or subtraction. Memory cellscorresponding to the identified overlapped bit state distribution can bedisabled, facilitating identification of other overlapped bit statedistributions.

In accord with particular aspects of the claimed subject matter, a setof bit state distributions can be shifted so as to render such set ofdistributions, or another set of distributions, to be non-overlapped.Shifting a set of distributions can occur by shifting a default programlevel associated with a particular bit state and re-programming cells tothe shifted program level, for instance. By rendering a set ofdistributions to be non-overlapped, such set can be uniquely identified.Subsequently, a mathematical operation can be performed on one or moreof the identified distributions and a related overlapped distribution(s)to facilitate identification of at least one overlapped bit statedistribution in accord with additional aspects disclosed herein.

The following description and the annexed drawings set forth in detailcertain illustrative aspects of the disclosed subject matter. Theseaspects are indicative, however, of but a few of the various ways inwhich the principles of the innovation may be employed and the disclosedsubject matter is intended to include all such aspects and theirequivalents. Other advantages and novel features of the disclosedsubject matter will become apparent from the following detaileddescription of the innovation when considered in conjunction with thedrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a block-diagram of a system that can identifypotentially overlapped bit state distributions of a multi-cell memorydevice in accord with aspects of the claimed innovation.

FIG. 2 consisting of FIG. 2A and FIG. 2B, depicts example logicalrelationships between a bit state distribution and an adjacent bit statedistribution in accord with aspects disclosed herein.

FIG. 3 illustrates a sample block diagram of a system that can identifypotentially overlapped bit state distributions by applying and analyzingsuch distributions with respect to a reference.

FIG. 4 depicts a sample relationship between bit state distributions andreference points used to distinguish such distributions in accord withaspects of the subject innovation.

FIG. 5 illustrates an example block diagram of a system that can shiftand measure a set of bit state distributions to facilitateidentification of one or more states associated with such distributionsin accord with aspects disclosed herein.

FIG. 6 consisting of FIG. 6A and FIG. 6B, depicts an example set of bitstate distributions wherein shifting such distributions can facilitateidentification of a state associated with one or more distributions.

FIG. 7 illustrates a sample methodology for identifying potentiallyoverlapped bit state distributions in accord with aspects of the claimedsubject matter.

FIG. 8 depicts a sample methodology for measuring, shifting, andidentifying states of bit state distributions of a plurality ofmulti-cell memory devices in accord with aspects disclosed herein.

FIGS. 9 and 10 depict a flowchart of an exemplary methodology forutilizing logical relationships between state distributions todistinguish between overlapping distributions of dual cell memorydevices in accord with aspects disclosed herein.

FIG. 11 is a block diagram of a suitable operating environment that caninterface with a quad-bit memory device.

FIG. 12 is a schematic block diagram of a sample networking environmentusable in conjunction with a quad-bit memory device.

DETAILED DESCRIPTION

The disclosed subject matter is described with reference to thedrawings, wherein like reference numerals are used to refer to likeelements throughout. In the following description, for purposes ofexplanation, numerous specific details are set forth in order to providea thorough understanding of the subject innovation. It may be evident,however, that the disclosed subject matter may be practiced withoutthese specific details. In other instances, well-known structures anddevices are shown in block diagram form in order to facilitatedescribing the subject innovation.

As utilized herein, terms “component,” “system,” “interface,” “engine,”and the like are intended to refer to a computer-related entity, eitherhardware, a combination of hardware and software, software (e.g., inexecution), and/or firmware. For example, a component can be a processrunning on a processor, a processor, an object, an executable, aprogram, and/or a computer. By way of illustration, both an applicationrunning on a server and the server can be a component. One or morecomponents can reside within a process and/or thread of execution and acomponent can be localized on one computer and/or distributed betweentwo or more computers. As another example, an interface can include I/Ocomponents as well as associated processor, application and/or APIcomponents, and can be as simple as a command line or a more complexIntegrated Development Environment (IDE).

Furthermore, the claimed subject matter may be implemented as a method,apparatus, or article of manufacture using standard programming and/orengineering techniques to produce software, firmware, hardware, or anycombination thereof to control a computer to implement the disclosedsubject matter. The term “article of manufacture” as used herein isintended to encompass a computer program accessible from anycomputer-readable device, carrier, or media. For example, computerreadable media can include but are not limited to magnetic storagedevices (e.g. hard disk, floppy disk, magnetic strips . . . ), opticaldisks (e.g., compact disk (CD), digital versatile disk (DVD) . . . ),smart cards, and flash memory devices (e.g., card, stick, key drive . .. ). Of course, those skilled in the art will recognize manymodifications may be made to this configuration without departing fromthe scope or spirit of the claimed subject matter.

Moreover, the word “exemplary” is used herein to mean serving as anexample, instance, or illustration. Any aspect or design describedherein as “exemplary” is not necessarily to be construed as preferred oradvantageous over other aspects or designs. Rather, use of the wordexemplary is intended to present concepts in a concrete fashion. As usedin this application, the term “or” is intended to mean an inclusive “or”rather than an exclusive “or”. That is, unless specified otherwise, orclear from context, “X employs A or B” is intended to mean any of thenatural inclusive permutations. That is, if X employs A; X employs B; orX employs both A and B, then “X employs A or B” is satisfied under anyof the foregoing instances. In addition, the articles “a” and “an” asused in this application and the appended claims should generally beconstrued to mean “one or more” unless specified otherwise or clear fromcontext to be directed to a singular form.

The disclosed subject matter relates to systems and/or methods thatfacilitate accurately retrieving data in multi-bit, multi cell memorydevices (e.g., quad-bit, dual cell non-volatile flash memory). Inaccordance with one aspect of the claimed subject matter, a calculationcomponent can perform a mathematical operation on at least two bit statedistributions associated with a plurality of such devices to generate aresulting distribution. A filtering component can employ the resultingdistribution in connection with distinguishing between overlapping statedistributions. Such distinction can identify a state of one or moreoverlapping distributions, such as a distribution operated on by thecalculation component. An identified bit state distribution can then bedisabled to facilitate identification of additional bit statedistributions.

With reference now to FIG. 1, a block diagram of a system 100 isillustrated that can identify potentially overlapped bit statedistributions of a multi-cell memory device(s) 102 (e.g. a multi-bit,dual cell device) in accord with aspects of the claimed innovation.Multi-cell memory device(s) 102 can include non-volatile memory, such asflash memory, read only memory (ROM), programmable ROM (PROM), erasableprogrammable read only memory (EPROM), electronically erasableprogrammable read only memory (EEPROM), and the like. As an example,multi-cell memory device 102 can include non-volatile memory (e.g.,flash memory, multi-bit flash memory, and like memory) that furtherincludes multi-level, multi-bit flash memory cells.

Multi-bit memory cells can typically be programmed to multiple targetlevels that can represent multiple data bits. As a more specificexample, a quad-bit cell can be programmed to four discreet levels(e.g., B1, B2, B3, B4) corresponding to varying amounts of electriccharge stored within a memory cell. Furthermore, a B1 level cancorrespond to an unprogrammed state (e.g. a lowest amount of electriccharge), and subsequent levels, such as B2, can correspond to aprogrammed state having an electric charge higher than B1. Additionally,B3 can correspond to a programmed state having an electric charge higherthan B1 and B2, and B4 can correspond to a highest programmed statehaving an electric charge still higher than B1, B2, and B3.

Each bit level (e.g., B1 through B4) of a multi-bit device cancorrespond to different digital information, or data. Consequently, as acell is charged to a particular charge level, and changed to a differentcharge level or to the lowest charge level, writing, re-writing, anderasing, respectively, of data to multi-bit memory cell 102 can beeffectuated. Furthermore, an amount of charge stored therein (e.g., acurrent level) can be measured and compared to discreet threshold levels(e.g., B1 through B4) that correspond to different data, to effectuatereading data stored within multi-bit memory cell 102.

In accord with aspects disclosed herein, multi-cell memory device(s) 102can include two or more adjacent memory cells that can be independentlyprogrammed to different bit levels. For example, two adjacent cells ofmulti-cell memory device 102 can be programmed to an appropriate level(e.g. electric charge) representing data bit levels B1 and B2respectively (hereinafter referred to as B1-B2 or a B1-B2 state, whereB1 refers to a state of a first cell or group of cells having a lowestcharge, and B2 refers to a state of a second, adjacent cell or group ofcells having a slightly higher charge; it should be noted that the sameB1-B2 program state is also a B2-B1 state viewed with respect to thesecond cell or group of cells). As stated above, B1 and B2 can indicatean unprogrammed state in one memory cell and a first programmed state inan adjacent memory cell, for instance. Typically, multi-cell memorydevices (e.g., multi-cell memory device(s) 102) can exhibit acomplementary bit disturbance phenomenon, where a typical bit level(e.g., level of charge) of one cell can be perturbed and shifted by aprogrammed bit level of an adjacent cell (e.g., when differing chargesin adjacent cells partially average their respective values). Morespecifically, complementary bit disturbance can cause a programmed bitlevel of one cell to deviate from a predetermined level typicallyassociated with the programmed bit level.

The following example illustrates a complementary bit disturbancephenomenon applicable to one or more multi-bit, multi-cell memorydevice(s) (102) as described above. For this example, a bit level B1typically corresponds to an amount of charge between 5 and 7 and a bitlevel B2 typically corresponds to an amount of charge between 11 and 13.A first cell programmed to a B1 level and an adjacent cell programmed toa B2 level can, for instance, result in bit disturbance, such that anactual charge stored within the first cell is greater than the typical5-7 range (e.g. 8), or an actual charge stored within the second cell isless than the typical 9-11 range (e.g., 10), or both. More specifically,a B1-B2 state can result in a bit level associated with the first memorycell between 6 and 8, a B1-B3 state in a first memory cell level between7 and 9, and a B1-B4 state in a cell level between 8 and 10. As aresult, it can be difficult to differentiate between certain bit levels,as illustrated by this example, if a shifted level nears a levelcorresponding to a different bit level (e.g., if the charge in the firstcell overlaps the 9-11 range associated with a B2 level). It should beappreciated that the specific embodiments provided by the foregoingexample are not to be construed so as to limit the disclosure; rather,like embodiments known to one of skill in the art or made known to oneof skill in the art through the context provided by this example areincorporated herein.

System 100 can include a calculation component 104 that performs amathematical operation on two or more state distributions associatedwith multi-cell memory device(s) 102, and generates a resultingdistribution that can facilitate identification of at least oneoverlapped state. For example, the resulting distribution can have adispersity (e.g., where dispersity can relate to a size or a range ofbit levels that can correspond to a bit state) smaller than dispersityparameters associated with the two or more state distributions thatproduce the resulting distribution (e.g., via the mathematicaloperation). Dispersity, as used herein, is defined as a width of adistribution (e.g., a range of measured voltage values of programmedmemory cells). A distribution with smaller dispersity typically overlapsfewer, if any, other distributions, and a state associated with suchdistribution is consequently easier to identify. As a more specificexample, if a first distribution is a B3-B1 distribution having atypical charge in the range of 15-17 (e.g., having a dispersity relatedto the range size, or 2), and a second distribution is a B1-B3distribution having a typical charge in the range of 7-9 (e.g., alsohaving a dispersity related to the range size, or 2), the resultingdistribution, having smaller dispersity, could have a typical charge inthe range of 8-9, or 23-24 for instance (e.g., having a dispersityrelated to the range size of the resulting distribution, or 1).

In accord with further aspects of the claimed subject matter, the two ormore state distributions utilized by calculation component 104 can berelated by at least one logical relationship. Such a logicalrelationship can, for instance, correlate relative positions of cell bitstate values on adjacent state distributions associated with a commongroup of cells of the multi-cell memory device(s) 102 (e.g., as depictedin more detail at FIG. 2, infra). For example, a first logicalrelationship can specify that a cell having a high relative position(e.g., having a cell bit value of 9 for the B1-B3 state illustrated inthe previous example above) on one state distribution can have a highrelative position on an adjacent state distribution as well (e.g.,having a cell bit value of 17 for the B3-B1 state, adjacent to the B1-B3state, illustrated in the previous example supra). As an additionalexample, a second logical relationship can specify that a cell having ahigh relative position on one state distribution can have a low relativeposition on an adjacent state distribution (e.g., cell bit value of 9for a B1-B3 state and cell bit value of 15 for B3-B1 state).

In addition, the smaller dispersity associated with a resultingdistribution, described above, can result at least in part based on thelogical relationship between the two or more state distributionsutilized by calculation component 104 to generate such resultingdistribution. More specifically, a typical dispersity parameter of adistribution resulting from, for instance, the addition of two statedistributions, can be the square root of the sum of the squares of thedispersities of the two or more state distributions. Mathematically,such a resulting distribution dispersity can be represented by thefollowing equation:

Ω_(R)=√{square root over (Ω₁ ²+Ω₂ ²+Ω_(N) ²)} (where N is an integer)

where Ω_(R)=the dispersity of the resulting distribution and Ω₁, Ω₂, andΩ_(N) are the dispersities of the two or more state distributionsutilized by calculation component 104 to generate the resultingdistribution. A dispersity of a resulting distribution governed by thesquare of the sum of the squares relationship can be larger thandispersities of operated state distributions. However, where a logicalrelationship exists between two state distributions added, for instance,(or, e.g., subtracted, or operated in like manner) by calculationcomponent 104, a dispersity of such resulting distribution can be muchsmaller than that described above. Consequently, a state of suchresulting distribution can be more likely determined (e.g., by analysiscomponent 106) as it will be less likely to overlap other distributions.

System 100 can include an analysis component 106 that can employ theresulting distribution in connection with distinguishing betweenoverlapping state distributions of the multi-cell memory device(s) 102.Analysis component 106 can utilize a constant reference to identify astate associated with non-overlapping state distributions. A constantreference can be any constant bit level (e.g., charge, voltage, etc.) ofa cell or group of cells of multi cell memory device(s) 102. As a morespecific example, a constant reference (provided, e.g., by referencecomponent 308, infra) can include, for instance, a constant measurablecurrent, a non-varying bit, an experimentally determined level, a levelinferred from another level or information related to a memory device(302), or combinations thereof. By choosing a constant reference betweenstate distributions of one programmed bit, or group of bits, and statedistributions of a second programmed bit, or group of bits, analysiscomponent 106 can identify a program state of cells corresponding tosuch state distributions. It should be appreciated that a referencecould be one or more of multiple referencing schemes, such as dynamicreferencing schemes or a static or constant scheme, mirrored referencingscheme, etc., that can track time and/or retention, can be applied todistinguish between bit levels.

Referring to the previous example, a non-overlapping reference betweenbits B1, B2 (having typical and disturbed bit levels that range from 5to 14, for instance) and bits B3, B4 (having typical and disturbed bitlevels that range from 15 to 23, for instance) can be 14.5, for example.By measuring the first memory cell with respect to level 14.5, a B1/B2bit level can be distinguished from a B3/B4 bit level, and vice versa.Consequently, if a resulting distribution generated by calculationcomponent 104 has small dispersity (e.g. due to a logical relationshipbetween state distributions operated on by calculation component 104)and does not overlap other distributions, analysis component 106 canidentify a state related to the resulting distribution (e.g., byemploying a reference).

The following example illustrates one particular aspect of the subjectinnovation, to provide context for the disclosure; it should beappreciated that the subject disclosure is not limited to the exampleembodiment. For a single dual-cell, quad-bit device 16 program statescan exist (e.g., 4 variations of one cell corresponding with 4variations of a second cell; 4×4=16 variations). For purposes of thisexample, 5 program states will be discussed, B1-B1, B1-B2, B1-B3, B1-B4,and B2-B1. Also for purposes of this example, typical B1-B1 and B1-B2state distributions associated with a memory cell or group of memorycells associated with multi cell memory device(s) 102 can typically be5-7 and 9-11, respectively (likewise, B3-B1 and B4-B1 states cantypically be 13-15 and 20-22, respectively). As a result ofcomplementary bit disturbance, discussed above, a measured levelassociated with the first memory cell can depend on a program state ofthe second memory cell. More specifically, assume for the currentexample that bit disturbance ranges associated with B1-B2, B1-B3, andB1-B4 levels, respectively, can typically be 6-8, 7-9, and 8-10,respectively. Consequently, a typical B1 level of the first memory cellcan overlap a typical B2 level of such cell, when the second memory cellis in either a B3 or B4 state (in accord with the charge values andranges specified via this example).

Overlapping program states associated with a cell or group of cells canfirst require their states be distinguished in some manner in order toidentify states of such cells. Employing a calculation component (104)to produce a resulting distribution having a relatively small dispersitycan facilitate such distinction. If a state of a resulting distributioncan be determined, a state of one or more related distributions can beidentified by inferring information from the state of the resultingdistribution. For example, if calculation component 104 performs anaddition operation on a first state distribution and a second statedistribution, a state of a third, resulting distribution will be the sumof the states of the first and second distributions. More specifically,if a resulting distribution is determined to be a B4 state (e.g.,identified by analysis component 106), and the first state distributioncan be identified as a B3 state, then the second distribution (even ifoverlapped by other distributions) can be identified as a B1 state.Consequently, the second distribution, once identified as a particularstate, can be disabled or ignored (e.g. by analysis component 106). Bydisabling or ignoring the second distribution, other statedistributions, overlapped by the second distribution, can potentially beidentified as well.

Continuing the previous example, B1 and B2 program states of a firstmemory cell can overlap and be indistinguishable via conventionalidentification methods when a second memory cell, adjacent and logicallyrelated to the first (e.g. as described in more detail at FIGS. 2A and2B, infra), is in a B3 or B4 state. More specifically, if the firstmemory cell has a program level of 7-9 as a result of bit disturbance,such cell can be in either a B1 state or a B2 state, as defined by thetypical B1 (5-7) and B2 (9-11) ranges. If, however, an adjacent cell canbe determined to be in a B3 state (program state range of 13-15), thencalculation component can perform a mathematical operation (e.g.,addition, subtraction, or the like) on program level ranges associatedwith the first and the second cells (or, e.g., on state distributionsassociated with a first and second group of cells) and generate aresulting distribution. If such resulting distribution has a rangesubstantially similar to a typical B4 state (e.g., 20-22, or the like),then the overlapped first cell range can be identified as a B1 state,instead of a B2 state. Alternatively, if such resulting distribution hasa range substantially larger than a typical B4 state (e.g., 25 orhigher), then the overlapped first cell can be identified as a B2 state.

As utilized herein, two adjacent cells are logically related if theyhave a relationship of proportionality or inverse proportionality, orthe like or a suitable other relationship. It should also be appreciatedthat a logical relationship, as defined herein, can be enforced onadjacent cells during a programming operation. For instance, if a cellis programmed to a high end of a 2 state, an adjacent cell can also beprogrammed to a substantially similar relative position (e.g., a highend) of a state, whether a 1 state, 2 state, 3 state, and so on, toprovide that the adjacent cells have a logical relation ofproportionality. Alternatively, the adjacent cell can be programmed to asubstantially inverse relative position (e.g., a low end) of the 1state, 2 state, 3 state, etc., to enforce a logical relationship ofinverse proportionality (e.g. see FIG. 2, infra).

In accord with further aspects of the claimed subject matter,calculation component 104 can perform a particular mathematicaloperation appropriate for a logical relationship between two statedistributions to generate a resulting distribution with relatively smalldispersity. If, for example, a logical relationship specifies thatprogram states of a cell or group of cells will have a substantiallyopposite relative position on adjacent state distributions (e.g.discussed in further detail at FIG. 2B, infra), adding such statedistributions to each other can produce a resulting distribution withrelatively small dispersity. To continue the previous example, if afirst program range (or, e.g., state distribution associated with afirst group of cells) is 7-9, and a second program range is 13-15(indicating, e.g., a B3 state), then a resulting distribution can be20-21, or 21-22, or the like, indicating a typical B4 state for suchresulting distribution. Alternatively, if the resulting distribution hasa range of 23-24, or 24-25, or the like, it can be associated withgreater than a B4 state, as described above. Other combinations oflogical relations between state distributions and mathematicaloperations appropriate for generating resulting distributions with smalldispersity are contemplated, and incorporated as part of the subjectdisclosure (e.g., see the discussion with respect to FIG. 2, infra, fordiscussion of examples).

FIGS. 2A and 2B depict example logical relationships between a first bitstate distribution and an adjacent bit state distribution in accord withaspects disclosed herein. Mathematical operations appropriate for suchlogical relationships can generate a resulting distribution withrelatively small dispersity as compared with a first and adjacentdistribution. FIG. 2A depicts a logical relationship of proportionality,wherein a program state of a cell or group of cells (e.g., representedby graph points 206A, 210A and 208A, 212A) will have substantiallysimilar relative positions on a first distribution 202A and a second,adjacent distribution 204A.

Distribution 202A indicates a number of cells of a plurality ofmulti-bit, dual cell memory devices having a particular current (or,e.g. charge stored within the cell representing a program state)associated with a 3-1 program state (or, e.g., a B3-B1 state as outlinedabove). More specifically, each point of distribution 202A indicates anumber of cells having a particular current for the 3-1 state. Asdescribed supra, a 3-1 state can result from a first group of cellsprogrammed to a bit state of 3, with adjacent cells programmed to a bitstate of 1. Distribution 204A indicates a number of cells having aparticular current associated with a 1-3 state (or, e.g., a B1-B3 stateas outline above) of the plurality of multi-bit, dual memory devices.Each point of distribution 204A indicates a number of cells having aparticular current for the 1-3 state.

The logical relationship of proportionality can be indicated by points206A, 208A, 210A and 212A. If each of the cells having currentsrepresented by points 206A and 208A, at a relatively high end ofdistribution 202A, also are represented by points having substantiallysimilar relative positions on distribution 204A (relatively highpositions on such distribution), for example as indicated by points 210Aand 212A, then these cells are relatively proportional with respect tothe 3-1 and 1-3 distributions. If a substantial majority of cellsforming two distributions are relatively proportional, as defined hereinand indicated by FIG. 2A, then subtracting such distributions willresult in a distribution having a dispersity much smaller than thedispersities of the 202A and 204A distributions. Consequently, a programstate related to the resulting distribution can be easier to identify.

In accord with additional aspects of the claimed subject matter, a stateof a resulting distribution can be used to infer an unknown state ofdistribution 202A and/or distribution 204A. As an example to illustratesuch inference, distribution 202A is known to represent cells having a 3state, but distribution 204A represents cells whose bit levels overlapthe 1 and 2 states. Since distributions 202A and 204A are substantiallyrelated by a logical relationship of proportionality, subtractingdistributions 202A and 204A can generate a resulting distribution withrelatively small dispersity. If the resulting distribution does notoverlap other distributions, and consequently its state can beidentified (e.g. via comparing with a reference, as described byanalysis component 106, supra), then distribution 204A can also beidentified. More specifically, if the resulting distribution correspondsto a 2 state, then distribution 204A can correspond to the difference ofthe resulting distribution state and 202A distribution state (e.g., a 2state subtracted from a 3 state can result in a 1 state, correspondingto 204A). Alternatively, if the resulting distribution corresponds to a1 state, then distribution 204A can be identified as a 2 state.

FIG. 2B illustrates an alternate logical relationship as compared withFIG. 2A, a relationship of inverse proportionality. To illustrate,distribution 202B depicts a number of cells of a plurality of multi bit,multi cell memory devices having a particular current associated with a3-1 program state. More specifically, each point of distribution 202Bindicates a number of cells having a particular current for the 3-1state. Distribution 204B indicates a number of cells having a particularcurrent associated with a 1-3 state of the plurality of multi-bit, dualmemory devices. Each point of distribution 204B indicates a number ofcells having a particular current for the 1-3 state.

The logical relationship of inverse proportionality can be indicated bypoints 206B, 208B, 210B and 212B. If each of the cells having currentsrepresented by points 206B and 208B, at a relatively low end ofdistribution 202B, are represented by points having substantiallyopposite relative positions on distribution 204B (relatively highpositions on such distribution), for example as indicated by points 210Band 212B respectively, then these cells are relatively inverseproportional with respect to the 3-1 and 1-3 distributions. If asubstantial majority of cells forming two distributions are relativelyinverse proportional, as defined herein and indicated by FIG. 2B, thenadding such distributions will result in a distribution having adispersity much smaller than the dispersities of the 202B and 204Bdistributions. Consequently, a program state related to the resultingdistribution can be easier to identify.

The following example illustrates identification of an unknown statebased on a logical relationship of inverse proportionality with a knownstate. Distribution 202B is known to represent cells having a 3 state,but distribution 204B represents cells whose bit levels overlap the 1and 2 states. Since distributions 202B and 204B are substantiallyrelated by a logical relationship of inverse proportionality, addingdistributions 202B and 204B can generate a resulting distribution withrelatively small dispersity. If the resulting distribution does notoverlap other distributions, and consequently its state can beidentified (e.g., via comparing with a reference, as described byanalysis component 106, supra), then distribution 204B can also beidentified. More specifically, if the resulting distribution correspondsto a 4 state, then distribution 204B can correspond to the difference ofthe resulting distribution state and 202B distribution state (e.g., a 4state subtracted from a 3 state can result in a 1 state, correspondingto 204B). Alternatively, if the resulting distribution corresponds to a5 state, then distribution 204B can be identified as a 2 state. Itshould be appreciated that other logical relationships can exist that,when used in conjunction with an appropriate mathematical operation,produce a resulting distribution of relatively small dispersity. Suchrelationships and operations, known in the art or made known to one ofskill in the art by way of the context provided herein, are incorporatedwithin the subject disclosure.

FIG. 3 illustrates a sample block diagram of a system 300 that canidentify potentially overlapped bit state distributions by applying andanalyzing such distributions with respect to a reference. Stateidentification component 304 can analyze state distributions associatedwith multi cell memory device(s) 302, identify states of non-overlappingdistributions by comparison to a reference. Additionally, stateidentification component 304 can distinguish at least one overlappingdistribution by mathematically operating on such a distribution and aknown distribution to generate a resulting distribution. Determinationof a state of the resulting distribution can facilitate determination ofa state of the unknown distribution and other distributions.

System 300 can employ an analysis component 306 that can compare anon-overlapped distribution to a reference, and determine a state ofsuch a distribution. The reference can be provided by a referencecomponent 308, which can iteratively choose a plurality of references(e.g., associated with a charge, current, voltage, etc., of a system ormemory cell) bounded by non-overlapping cell bit distributions. Inreference to the example presented in FIG. 1, supra, a reference boundedby non-overlapping cell bit levels for non-shifted B1 and B2 states(e.g., B1-B1 and B2-B1 states) can be a bit level of 8. A referenceprovided by reference component 308 can include, for instance, aconstant measurable current, a non-varying bit, an experimentallydetermined level, a level inferred from another level or informationrelated to a memory device (302), or combinations thereof.

Calculation component 310 can perform mathematical operations on knownand unknown distributions to facilitate identification of a state of theunknown distribution. An unknown distribution can be a distributioncorresponding to a first state that overlaps a distributioncorresponding to a second state, where conventional techniques areinsufficient to distinguish such distributions (e.g. a B1-B3 or B1-B4state of the example provided in FIG. 1, supra, where such statesoverlap the B2-B1 state). The known distribution can be, for instance, adistribution whose state is identified via analysis component 306 andreference component 308, as outlined above. The mathematicaloperation(s) can result in a third distribution, different from theknown and unknown distributions. Additionally, the third distributioncan have a dispersity parameter lower than dispersities of the known andunknown distributions (e.g. where such distributions are related by alogical relationship of proportionality or inverse proportionality, orthe like, as indicated above). Consequently, a state corresponding tothe third distribution can be identified and such state can be utilized(e.g., by analysis component 306) to infer a state of the unknowndistribution.

Filtering component 312 can disable known state distributions to helpfurther determine states associated with other unknown (e.g.,overlapped) state distributions. For example, if a B1-B4 state of a quadbit, dual cell memory device overlaps a B2-B1 state, identifying anddisabling the B1-B4 state can subsequently render the B2-B1 statenon-overlapped. Consequently, cells programmed to levels correspondingto a B2 state can at least be distinguished from a B1 state by analyzingsuch cells with respect to a reference (e.g. provided by referencecomponent 308) bisecting the B1 and B2 bit states. Therefore, statedistributions identified by analysis component 306 (e.g., by utilizing astate of a resulting distribution, determined as described herein) canbe disabled to facilitate state identification of distributionsoverlapped by the disabled distribution. In such a manner, stateidentification component 304 can distinguish potentially overlappingstate distributions of multi-cell memory device(s) 302, in accordancewith aspects of the claimed subject matter.

FIG. 4 illustrates an example relationship between bit statedistributions and reference points used to distinguish suchdistributions in accord with aspects of the subject innovation. Theexample depicted in FIG. 4 refers to a plurality of tri-bit, dual cellmemory devices, however, other suitable multi bit, multi cell memorydevices not specifically delineated herein, within the spirit and scopeof the claims, are incorporated in the subject specification. The graphin FIG. 4 depicts distributions of numbers of memory cells havingparticular current values for various programmed states. Distributions402, 404, and 406 refer to programmed 3/3, 3/2, and 3/2 statesrespectively. Such distributions illustrate the complementary bitdisturbance phenomenon discussed supra. More specifically, the currentvalues of the 3/2 and 3/1 distributions are shifted toward the 2 and 1bit states on the graph. Note, that with respect to FIG. 4, sub-statedistributions of a state (e.g., 3/1, 3/2, 3/3 distributions of the3-state) are depicted via a slash ‘/’ instead of ‘-’ utilized elsewherein the subject specification. Such a convention is utilized with respectto FIG. 4 to distinguish sub-state distributions from mathematicallyoperated resulting distributions, such as a 2+3 distribution (420), 1+3distribution (422), or other distributions such as a subtractedresulting distribution (e.g., 3-1 distribution (not shown) or 3-2distribution (not shown)) or like resulting distributions.

Distributions 408, 410, 412, and 414, 416, and 418 illustrate programmed2/3, 2/2, 2/1, and 1/3, 1/2, and 1/1 states respectively. Each of the2-state (408, 410, 412) and 1-state (414, 416, 418) distributionsexhibit a complementary bit disturbance, most notably the 1-state. Thedegree of disturbance for each state is depicted by the amount ofseparation between distributions of a common state. More specifically,the separation in current between the 3/3 (402), 3/2 (404), and 3/1(406) states illustrates the degree of disturbance associated with the3-state distributions (402, 404, 406). For the example depicted in FIG.4, the 1-state (414, 416, 418) distributions experience a much greaterdegree of bit disturbance, which can by typical for multi cell memorydevices. However, this is but one example embodiment, and many suitablevariations on the complementary bit disturbance among program states canexist within the spirit and scope of the subject disclosure.

FIG. 4 further depicts a 2+3 distribution 420 and a 1+3 distribution422. Such distributions can result, for instance, from a mathematicaloperation (e.g., an addition operation) performed upon two or more otherprogram state distributions having a logical relationship (e.g., asdescribed at FIG. 2, supra). As a specific example, addition of a1-state (414, 416, 418) distribution and a 3-state (402, 404, 406)distribution can produce a 1+3 (422) distribution. Furthermore, additionof a 3-state (402, 404, 406) and a 2-state (408, 410, 412) distributioncan produce the 2+3 (420) distribution. As long as corresponding statesof the 2+3 state distribution 420 and the 1+3 state distribution 422 canbe distinguished from each other (e.g. as long as they do not overlapeach other) and from other distributions, knowledge of such a state canbe used to facilitate state identification of other unknown, overlappedstate distributions.

A specific example of utilizing 1+3 state 422 and 2+3 state 420 todetermine unknown distribution states can be as follows. As depicted,the 1/3 state distribution 414 overlaps the 2/1 state distribution 412.This can make the 2-state and 1-state distributions indistinguishable byconventional methods, as a reference point between such distributionsthat identify a state related thereto does not exist. The 3-statedistributions (402, 404, 406), however, are not overlapped by the2-state or 1-state distributions (408, 410, 412, and 414, 416, 418respectively), and consequently, a reference point 426 (e.g., constantcurrent level, constant bit, etc.) bisecting the 3-state distributionsand 2-state distributions can facilitate identification of a cell as a3-state distribution (e.g., via analysis component 306).

Once a state of a particular distribution is determined, mathematicallyoperating on such a state and an unknown state can produce a resultingdistribution. If a state of the resulting distribution can subsequentlybe determined, such determination can be utilized to infer a state ofthe one or more unknown distributions. More specifically, a known3-state distribution can be added (or, e.g. subtracted, multiplied,divided, etc.) with an unknown distribution to produce a resultingdistribution. A state of distribution resulting from such addition canconsequently be the sum of the states of the added distributions, or,represented mathematically:

-   -   S_(R)=3+‘x’ (where x can be an unknown state of a distribution        added to the known 3-state distribution, and S_(R) is the state        of the resulting distribution).        If a state of the resulting distribution can be determined, for        example via reference point 424 and/or similar reference        point(s) bisecting the resulting distribution and other        distributions, then the foregoing equation can be solved for ‘x’        to provide the state of the unknown distribution. More        specifically, if the state of the resulting distribution is        determined to be 4, then the foregoing equation provides that        x=4−3, or 1. In such case, the unknown distribution is a 1-state        distribution (414, 416, 418). Alternatively, if the resulting        distribution is determined to be 5, then the foregoing equation        provides that x=5−3, or 2. In such case, the unknown        distribution is a 2-state distribution (408, 410, 412). Once an        overlapped state of a group of cells forming a set of state        distributions is determined, sub-states (e.g., 1/1, 1/2, 1/3        states of a 1-state distribution) can also typically be        determined by conventional techniques, such as measuring values        of the adjacent cells, (e.g., by way of measurement component        506, discussed in more detail at FIG. 5 infra, or like        techniques), and/or by iteratively repeating the above and/or        like mathematical operation(s) on subsequent sub-state        distributions having logical relationships, as defined herein or        the like.

Once a state of a state distribution is determined, it can be disabledto facilitate distinction of further unknown state distributions. Forexample, if a state of the 1/3 distribution 414, overlapping the 2/1distribution 412, is determined (e.g., utilizing reference point 428 anda suitable analysis component, such as analysis component 306 discussedsupra), it can be disabled. Subsequently, any reference point bisectingthe 1/2 distribution 412 and 2/1 distribution 416, such as referencepoint 428, can facilitate identification of 1-states and 2-states. Inthe subject example depicted at FIG. 4, identifying and disabling adistribution of a highly disturbed state (e.g. the 1-statedistributions, as discussed above) can typically be most beneficial infacilitating identification of overlapped states, as such disturbedstates are typically more likely to overlap other states.

In addition to the complementary bit disturbance phenomena discussedabove, dispersity parameters of state distributions can causedistributions to overlap. A dispersity parameter relates to a width of adistribution, or for example, a range of current that can correspond toa particular state. As depicted by the sample graph of FIG. 4, the1-state distributions (414, 416, 418) illustrate an example ofrelatively high dispersity as compared with the 3-state (402, 404, 406)and 2-state (408, 410, 412) distributions. Such relatively highdispersity can be a cause of overlap, as seen with the 2/1 and 1/3distributions. Put differently, states of distributions with smallerdispersities can be easier to identify than those with largerdispersities, as such smaller dispersity distributions are less likelyto overlap related distributions.

The 2+3 distribution 420 and the 1+3 distribution 422 both haverelatively small dispersities as compared with all other statedistributions depicted (402-418). Specifically, such small dispersitycan result from a logical relationship (e.g. proportionality or inverseproportionality, or the like, discussed supra) between two distributions(402-418) and an appropriate mathematical operation (e.g. addition fordistributions related by inverse proportionality, subtraction fordistributions related by proportionality, or like operation/relationpairs) utilized to generate a resulting distribution (420, 422). Asspecified above, a mathematically generated resulting distribution canfacilitate identification of at least one unknown state utilized toproduce such distribution, if a state of the resulting distribution canbe distinguished from other potential states. More specifically, inregard to the example depicted in FIG. 4, if a 1+3 distribution (422)can be distinguished from a 2+3 distribution, then S_(R) can bedetermined for solving the equation:

S _(R)=3+‘x’

Small dispersities of resulting distributions (420, 422) make states ofsuch distributions easier to identify (e.g. via reference point 424),and consequently can provide more information with which to identifystates of overlapped state distributions (e.g. 2-state and 1-statedistributions, as depicted in FIG. 4).

FIG. 5 depicts an example block diagram of a system 500 that can shiftand measure a set of bit state distributions to facilitateidentification of one or more states associated with such distributionsin accord with aspects disclosed herein. State identification component504 can identify a state of overlapping distributions as describedherein. More specifically, state identification component 504 canidentify a state of a non-overlapping distribution, and mathematicallyoperate on such distribution and an unknown distribution to produce aresulting distribution. Subsequently, state identification component 504can determine a state of the resulting distribution and utilize thestate to infer a state of the unknown distribution, as discussed herein.Additionally, state identification component 504 can identify a logicalrelationship (e.g., proportionality and/or inverse proportionality, orthe like) between the known and unknown state distributions, and performan appropriate mathematical operation such that the resultingdistribution has relatively small dispersity, and consequently a stateof which can be distinguished more easily from other potential states.

In addition, system 500 can adjust a set of state distributionscorresponding to a program state to facilitate identification of a stateof at least one state distribution. For example, measurement component506 can identify state levels (e.g. value of current, charge, voltage,etc., stored within a cell or group of cells) of cells of multi cellmemory device(s) 502, and program component 508 can alter default statelevels corresponding to a particular state, and reprogram states ofcells to such new current levels. Measurement component 506 can include,for example, a device or process, or combination of a device and processthat can measure a current, voltage, charge, or like electroniccharacteristic, or similar electronic measurement component that canmeasure such characteristic. As a more specific example of adjustingstate distributions, if a first state (S1) corresponds to a range of5-9, a second state (S2) corresponds to a range of 11-13, and a thirdstate (S3) corresponds to a range of 13-15, overlapping the secondstate, then program component 508 can alter the state levels of thesecond state to 10-12 and reprogram them accordingly. Consequently,state identification component 504 can identify a state of distributionscorresponding to the specified ranges, for instance, by choosingreference points at 9.5, 10.5, 12.5, or the like.

Some situations can exist where shifting a program state (e.g., byaltering default state levels and reprogramming corresponding cells tothe altered level) can be insufficient to facilitate distinction of allstates. For example, if a 2/1 distribution of a 2-state overlaps a 1/3distribution of a 1-state (e.g., as depicted at 412 and 414 of FIG. 4),and a 2/3 distribution of the 2-state also overlaps a 3/1 distribution,then all states can be indistinguishable from each other. However, incertain circumstances (discussed in more detail at FIG. 6, infra) if a2-state can be shifted further into the 1-state (or, e.g. further intothe 3-state) to separate the 2-state and the 3-state, and so that the2/3 state is not overlapped by the 1/3 state, the 3-state can still bedistinguished (as discussed supra) and state identification component504 can then distinguish at least one overlapped state as specifiedabove (e.g., mathematically operating on a 3-state and unknown statedistribution, identifying a state of the resulting distribution, andinferring a state of the unknown distribution). Consequently, system 500can provide flexibility for state identification, as discussed herein,by shifting state distributions to cause at least one state to benon-overlapped and distinguishable by state identification component504.

FIGS. 6A and 6B depict an example set of bit state distributions whereinshifting such distributions can facilitate identification of a stateassociated with one or more distributions. FIG. 6A illustrates anexample scenario where all 3-state distributions (602A, 604A, 606A)overlap at least one 2-state distribution (608A, 610A, 612A), making all2-state and 3-state distributions (602A, 604A, 606A, and 608A, 610A,612A, respectively) indistinguishable by conventional methods. However,due to the wide dispersity and high disturbance associated with the1-state distributions (614A, 616A, 618A), the 2-state distributions canbe shifted (e.g., by program component 508 discussed supra)substantially to facilitate identification of 2 and 3 states, withoutfully overlapping the 1-3 state distribution.

FIG. 6B illustrates an example scenario where the 2-state distributions(608A, 610A, 612A) have been shifted (e.g., as described supra) to fullydistinguish the 2 and 3-states, without overlapping more than the 2-1(612B) and 1-3 (614B) states. As mentioned, the disparity in bitdisturbance illustrated by the 1-state distributions (614B, 616B, 618B)and the 2-state and 3-state distributions (602A, 604A, 606A, and 608A,610A, 612A, respectively) facilitate such scenario.

Additionally, such disparity can be typical of 1-state distributions(614B, 616B, 618B) as compared with higher state distributions (602A,604A, 606A, 608A, 610A, 612A, or the like). Consequently, shifting astate and corresponding distributions can facilitate distinction of atleast one state and corresponding state distributions to distinguishunknown distributions as described herein. For instance, by shifting astate and corresponding distribution overlapped states can be selected.By selecting states to overlap that also have a logical relationship asdefined herein, the selected states can further be distinguished byapplying a mathematical operation to such overlapped and logicallyrelated states, as discussed herein. Additionally, it should beappreciated that the 2-state distribution can be shifted further intothe 1-state distribution, so long as the 2-3 state (608B) and 1-3 state(614B) distributions do not overlap, in accord with distinguishingoverlapping 1-state and 2-state distributions (608A, 610A, 612A, and614B, 616B, 618B, respectively) as described herein.

FIGS. 7-10 illustrate example methodologies in accordance with thedisclosed subject matter. For purposes of simplicity of explanation, themethodologies are depicted and described as a series of acts. It is tobe understood and appreciated that the claimed subject matter is notlimited by the acts illustrated and/or by the order of acts, for actsassociated with the example methodologies can occur in different ordersand/or concurrently with other acts not presented and described herein.For example, those skilled in the art will understand and appreciatethat a methodology could alternatively be represented as a series ofinterrelated states or events, such as in a state diagram. Moreover, notall illustrated acts can be required to implement a methodology inaccordance with the claimed subject matter. Additionally, it should befurther appreciated that the methodologies disclosed hereinafter andthroughout this specification are capable of being stored on an articleof manufacture to facilitate transporting and transferring suchmethodologies to computers.

FIG. 7 illustrates a sample methodology for identifying potentiallyoverlapped bit state distributions in accord with aspects of the claimedsubject matter. At 702, a program state of a non-overlapped statedistribution is identified. Such state distribution can correspond toprogram levels of a cell or group of cells of a plurality of multi cellmemory devices. Such multi-cell memory devices can include non-volatilememory, such as flash memory, ROM, PROM, EPROM, EEPROM, and the like.Furthermore, such multi-cell memory devices can include multi-level,multi-bit flash memory cells that can typically be programmed tomultiple target levels that can represent multiple data bits. As a morespecific example, a cell of a multi cell memory device can be programmedto multiple bit states, such as a quad-bit cell that can be programmedto four discreet levels (e.g. B1, B2, B3, B4) corresponding to varyingamounts of electric charge stored within a memory cell. For example, B1can correspond to an unprogrammed state, B2 to a programmed state havingan electric charge higher than B1, B3 to a programmed state having anelectric charge higher than B1 and B2, and B4 to a highest programmedstate having an electric charge still higher than B1, B2, and B3.

Identification of a program state in accord with reference number 702can be performed by an analysis component that can utilize a referenceto identify a state associated with non-overlapping state distributions.A reference can be any constant bit level of a cell or group of cellsthat bisects distributions corresponding to one state from distributionscorresponding to another state. Consequently, the reference candistinguish, for example, between threshold levels or groups of levelsthat overlap and other threshold levels or groups of levels that do notoverlap. An analysis component utilizing such a reference can thereforeidentify a state of at least one non-overlapping distribution.

At 704, the non-overlapped state distribution is added or subtractedwith an overlapped distribution. Adding or subtracting suchdistributions at reference number 704 can generate a resultingdistribution. Moreover, the resulting distribution can provideinformation that can be utilized to infer a state of the overlappeddistribution. More specifically, states of the known, unknown andresulting distributions can be related depending on addition orsubtraction. As an example, states of distributions added to generate aresulting distribution can be related to a state of the resultingdistribution by the following formula:

-   -   S_(R)=S_(K)+S_(U) (where S_(R) is the state of the resulting        distribution, S_(K) is the state of the known distribution, and        S_(U) is the state of the unknown distribution)        As a further example, states of distributions subtracted to        generate a resulting distribution can be related to a state of        the resulting distribution by the following formula:

S _(R) =S _(K) −S _(U).

At 706, a state of the overlapped distribution is determined from aresulting distribution. Such state can be determined, for example, bythe equations described above. More specifically, if a state of theresulting distribution can be determined, then the state of the unknowndistribution can be solved for utilizing the examples stated above. Itshould be appreciated that the subject methodology provides but oneexample of facilitating distinction of at least one overlapping statedistribution associated with a plurality of multi cell memory devices,and that other examples within the spirit and scope of the subjectspecification are incorporated herein.

FIG. 8 depicts a sample methodology for measuring, shifting, andidentifying states of bit state distributions of a plurality ofmulti-cell memory devices in accord with aspects disclosed herein. At802, cell values of a plurality of multi cell memory devices aremeasured. Such measurement can be performed, for example, by a measuringcomponent including a device, process, or electronic component orcombination thereof that can measure a current, voltage, charge, or likeelectronic characteristic. It is to be appreciated that cell leveldistributions can overlap other level distributions, makingcorresponding levels indistinguishable by conventional mechanisms.

At 804, values measured at reference number 802 are arranged into statedistributions. It is to be appreciated that cell level distributions canoverlap other level distributions, making corresponding levelsindistinguishable by conventional mechanisms. At 806, one or more statedistributions are shifted to create a non-overlapped distribution. At808, a program state of the non-overlapped distribution is identified.Such identification can be via an analysis component, as describedherein, utilizing a reference that bisects the non-overlappeddistribution and related distributions. Consequently, the one or morestate distributions shifted at reference number 806 can facilitateidentification of such a reference and the resulting identification ofthe non-overlapped distribution at reference number 808.

At 810, the non-overlapped distribution is added or subtracted from afirst overlapped distribution. Such addition or subtraction can producea resulting distribution. Additionally, where such non-overlapped andoverlapped distributions are related by a logical relationship (e.g.,relationship of proportionality or inverse proportionality, as describedherein, or like relationship), the resulting distribution can have arelatively small dispersity parameter associated with it, making a statecorresponding to such distribution more easily identified from otherpotential resulting distributions. At 812, a state of the first and/oranother overlapped distribution can be identified. For example, a stateof the first unknown distribution can be solved for via the state of theknown distribution and the state of the resulting distribution asdescribed herein. Additionally, once the state of the unknowndistribution is identified, such distribution can be disabled, and areference chosen that bisects state distributions overlapped by theunknown distribution (e.g., as indicated by reference point 428 of FIG.4) to facilitate identification of states of such bisecteddistributions.

FIGS. 9 and 10 depict a flowchart of an exemplary methodology forutilizing logical relationships between state distributions todistinguish between overlapping distributions of dual cell memorydevices in accord with aspects disclosed herein. At 902, statedistributions of a plurality of multi cell memory devices are measured.At 904, one or more of the state distributions are shifted to create anon-overlapped distribution, as disclosed herein. At 906, a stateassociated with the non-overlapped distribution is identified. Suchidentification can be by way of conventional techniques and/or viamethods described in the subject disclosure. At 908, identify anoverlapped distribution related to the non-overlapped distribution by alogical relationship. Such logical relationship can include, forexample, a relationship of proportionality or inverse proportionality asdescribed in the subject specification. At 910, a determination is madeas to whether such a distribution exists. If no distribution exists,methodology 900 proceeds to 912 where overlapping states can beidentified utilizing traditional methods. If such a distribution isdetermined to exist at reference number 910, the methodology 900proceeds to 914 where a second overlapped distribution is identified.

Methodology 900 then proceeds to 1002 at FIG. 10. Specifically, at 1002the identified non-overlapping distribution is mathematically operated(e.g. adding, subtracting, or the like) with each overlappeddistribution. At 1004, analyze two resulting distributions from themathematical operations. At least one of the resulting distributions canbe relatively less disperse than state distributions measured at 902 asa result of the logical relationship identified at 908. At 1006, a stateof the resulting distributions is identified. Such identification can befacilitated by identifying a reference that bisects the resultingdistributions, as defined herein. At 1008, a state of at least one ofthe overlapped distributions is identified, for instance, utilizing thestate of the resulting distributions. At 1010, at last one identifiedoverlapped distribution is disabled. At 1012, a state of at least oneadditional overlapped distribution can be identified, for instance, as aresult of disabling a distribution that had overlapped it (e.g., the 3/1state distribution (416) overlapping the 2/1 state distribution (414)indicated and described in detail at FIG. 4 supra).

What has been described above includes examples of the subjectinnovation. It is, of course, not possible to describe every conceivablecombination of components or methodologies for purposes of describingthe subject innovation, but one of ordinary skill in the art canrecognize that many further combinations and permutations of the subjectinnovation are possible. Accordingly, the disclosed subject matter isintended to embrace all such alterations, modifications and variationsthat fall within the spirit and scope of the appended claims.Furthermore, to the extent that the term “includes” is used in eitherthe detailed description or the claims, such term is intended to beinclusive in a manner similar to the term “comprising” as “comprising”is interpreted when employed as a transitional word in a claim.

As utilized herein, terms “component,” “system,” “interface,” and thelike are intended to refer to a computer-related entity, eitherhardware, software (e.g., in execution), and/or firmware. For example, acomponent can be a process running on a processor, a processor, anobject, an executable, a program, and/or a computer. By way ofillustration, both an application running on a server and the server canbe a component. One or more components can reside within a process and acomponent can be localized on one computer and/or distributed betweentwo or more computers.

Artificial intelligence based systems (e.g., explicitly and/orimplicitly trained classifiers) can be employed in connection withperforming inference and/or probabilistic determinations and/orstatistical-based determinations as in accordance with one or moreaspects of the disclosed subject matter as described herein. As usedherein, the term “inference,” “infer” or variations in form thereofrefers generally to the process of reasoning about or inferring statesof the system, environment, and/or user from a set of observations ascaptured via events and/or data. Inference can be employed to identify aspecific context or action, or can generate a probability distributionover states, for example. The inference can be probabilistic—that is,the computation of a probability distribution over states of interestbased on a consideration of data and events. Inference can also refer totechniques employed for composing higher-level events from a set ofevents and/or data. Such inference results in the construction of newevents or actions from a set of observed events and/or stored eventdata, whether or not the events are correlated in close temporalproximity, and whether the events and data come from one or severalevent and data sources. Various classification schemes and/or systems(e.g., support vector machines, neural networks, expert systems,Bayesian belief networks, fuzzy logic, data fusion engines . . . ) canbe employed in connection with performing automatic and/or inferredaction in connection with the disclosed subject matter.

Furthermore, the disclosed subject matter may be implemented as amethod, apparatus, or article of manufacture using standard programmingand/or engineering techniques to produce software, firmware, hardware,or any combination thereof to control a computer to implement thedisclosed subject matter. The term “article of manufacture” as usedherein is intended to encompass a computer program accessible from anycomputer-readable device, carrier, or media. For example, computerreadable media can include but are not limited to magnetic storagedevices (e.g. hard disk, floppy disk, magnetic strips . . . ), opticaldisks (e.g., compact disk (CD), digital versatile disk (DVD) . . . ),smart cards, and flash memory devices (e.g., card, stick, key drive . .. ). Additionally it should be appreciated that a carrier wave can beemployed to carry computer-readable electronic data such as those usedin transmitting and receiving electronic mail or in accessing a networksuch as the Internet or a local area network (LAN). Of course, thoseskilled in the art will recognize many modifications may be made to thisconfiguration without departing from the scope or spirit of thedisclosed subject matter.

Moreover, the word “exemplary” is used herein to mean serving as anexample, instance, or illustration. Any aspect or design describedherein as “exemplary” is not necessarily to be construed as preferred oradvantageous over other aspects or designs. Additionally, some portionsof the detailed description have been presented in terms of algorithmsand/or symbolic representations of operations on data bits within acomputer memory. These algorithmic descriptions and/or representationsare the means employed by those cognizant in the art to most effectivelyconvey the substance of their work to others equally skilled. Analgorithm is here, generally, conceived to be a self-consistent sequenceof acts leading to a desired result. The acts are those requiringphysical manipulations of physical quantities. Typically, though notnecessarily, these quantities take the form of electrical and/ormagnetic signals capable of being stored, transferred, combined,compared, and/or otherwise manipulated.

It has proven convenient, principally for reasons of common usage, torefer to these signals as bits, values, elements, symbols, characters,terms, numbers, or the like. It should be borne in mind, however, thatall of these and similar terms are to be associated with the appropriatephysical quantities and are merely convenient labels applied to thesequantities. Unless specifically stated otherwise as apparent from theforegoing discussion, it is appreciated that throughout the disclosedsubject matter, discussions utilizing terms such as processing,computing, calculating, determining, and/or displaying, and the like,refer to the action and processes of computer systems, and/or similarconsumer and/or industrial electronic devices and/or machines, thatmanipulate and/or transform data represented as physical (electricaland/or electronic) quantities within the computer's and/or machine'sregisters and memories into other data similarly represented as physicalquantities within the machine and/or computer system memories orregisters or other such information storage, transmission and/or displaydevices.

In order to provide a context for the various aspects of the disclosedsubject matter, FIGS. 11 and 12, as well as the following discussion,are intended to provide a brief, general description of a suitableenvironment in which the various aspects of the disclosed subject mattermay be implemented. While the subject matter has been described above inthe general context of computer-executable instructions of a computerprogram that runs on a computer and/or computers, those skilled in theart will recognize that the subject innovation also may be implementedin combination with other program modules. Generally, program modulesinclude routines, programs, components, data structures, etc. thatperform particular tasks and/or implement particular abstract datatypes. Moreover, those skilled in the art will appreciate that theinventive methods may be practiced with other computer systemconfigurations, including single-processor or multiprocessor computersystems, mini-computing devices, mainframe computers, as well aspersonal computers, hand-held computing devices (e.g., PDA, phone,watch), microprocessor-based or programmable consumer or industrialelectronics, and the like. The illustrated aspects may also be practicedin distributed computing environments where tasks are performed byremote processing devices that are linked through a communicationsnetwork. However, some, if not all aspects of the claimed innovation canbe practiced on stand-alone computers. In a distributed computingenvironment, program modules may be located in both local and remotememory storage devices.

With reference to FIG. 11, a suitable environment 1100 for implementingvarious aspects of the claimed subject matter can include a computer1112. The computer 1112 includes a processing unit 1114, a system memory1116, and a system bus 1118. The system bus 1118 couples systemcomponents including, but not limited to, the system memory 1116 to theprocessing unit 1114. The processing unit 1114 can be any of variousavailable processors. Dual microprocessors and other multiprocessorarchitectures also can be employed as the processing unit 1114.

The system bus 1118 can be any of several types of bus structure(s)including the memory bus or memory controller, a peripheral bus orexternal bus, and/or a local bus using any variety of available busarchitectures including, but not limited to, Industrial StandardArchitecture (ISA), Micro-Channel Architecture (MSA), Extended ISA(EISA), Intelligent Drive Electronics (IDE), VESA Local Bus (VLB),Peripheral Component Interconnect (PCI), Card Bus, Universal Serial Bus(USB), Advanced Graphics Port (AGP), Personal Computer Memory CardInternational Association bus (PCMCIA), Firewire (IEEE 1394), and SmallComputer Systems Interface (SCSI).

The system memory 1116 includes volatile memory 1120 and nonvolatilememory 1122. The basic input/output system (BIOS), containing the basicroutines to transfer information between elements within the computer1112, such as during start-up, is stored in nonvolatile memory 1122. Byway of illustration, and not limitation, nonvolatile memory 1122 caninclude ROM, PROM, electrically programmable ROM (EPROM), electricallyerasable programmable ROM (EEPROM), or flash memory. Volatile memory1120 includes RAM, which acts as external cache memory. By way ofillustration and not limitation, RAM is available in many forms such asSRAM, dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rateSDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM),Rambus direct RAM (RDRAM), direct Rambus dynamic RAM (DRDRAM), andRambus dynamic RAM (RDRAM).

Computer 1112 also includes removable/non-removable,volatile/non-volatile computer storage media. FIG. 11 illustrates, forexample, a disk storage 1124. Disk storage 1124 includes, but is notlimited to, devices like a magnetic disk drive, floppy disk drive, tapedrive, Jaz drive, Zip drive, LS-100 drive, flash memory card, or memorystick. In addition, disk storage 1124 can include storage mediaseparately or in combination with other storage media including, but notlimited to, an optical disk drive such as a compact disk ROM device(CD-ROM), CD recordable drive (CD-R Drive), CD rewritable drive (CD-RWDrive) or a digital versatile disk ROM drive (DVD-ROM). To facilitateconnection of the disk storage devices 1124 to the system bus 1118, aremovable or non-removable interface is typically used, such asinterface 1126.

It is to be appreciated that FIG. 11 describes software that acts as anintermediary between users and the basic computer resources described inthe suitable operating environment 1100. Such software includes anoperating system 1128. Operating system 1128, which can be stored ondisk storage 1124, acts to control and allocate resources of thecomputer system 1112. System applications 1130 take advantage of themanagement of resources by operating system 1128 through program modules1132 and program data 1134 stored either in system memory 1116 or ondisk storage 1124. It is to be appreciated that the disclosed subjectmatter can be implemented with various operating systems or combinationsof operating systems.

A user enters commands or information into the computer 1112 throughinput device(s) 1136. Input devices 1136 include, but are not limitedto, a pointing device such as a mouse, trackball, stylus, touch pad,keyboard, microphone, joystick, game pad, satellite dish, scanner, TVtuner card, digital camera, digital video camera, web camera, and thelike. These and other input devices connect to the processing unit 1114through the system bus 1118 via interface port(s) 1138. Interfaceport(s) 1138 include, for example, a serial port, a parallel port, agame port, and a universal serial bus (USB). Output device(s) 1140 usesome of the same type of ports as input device(s) 1136. Thus, forexample, a USB port may be used to provide input to computer 1112 and tooutput information from computer 1112 to an output device 1140. Outputadapter 1142 is provided to illustrate that there are some outputdevices 1140 like monitors, speakers, and printers, among other outputdevices 1140, which require special adapters. The output adapters 1142include, by way of illustration and not limitation, video and soundcards that provide a means of connection between the output device 1140and the system bus 1118. It should be noted that other devices and/orsystems of devices provide both input and output capabilities such asremote computer(s) 1144.

Computer 1112 can operate in a networked environment using logicalconnections to one or more remote computers, such as remote computer(s)1144. The remote computer(s) 1144 can be a personal computer, a server,a router, a network PC, a workstation, a microprocessor based appliance,a peer device or other common network node and the like, and typicallyincludes many or all of the elements described relative to computer1112. For purposes of brevity, only a memory storage device 1146 isillustrated with remote computer(s) 1144. Remote computer(s) 1144 islogically connected to computer 1112 through a network interface 1148and then physically connected via communication connection 1150. Networkinterface 1148 encompasses wire and/or wireless communication networkssuch as local-area networks (LAN) and wide-area networks (WAN). LANtechnologies include Fiber Distributed Data Interface (FDDI), CopperDistributed Data Interface (CDDI), Ethernet, Token Ring and the like.WAN technologies include, but are not limited to, point-to-point links,circuit switching networks like Integrated Services Digital Networks(ISDN) and variations thereon, packet switching networks, and DigitalSubscriber Lines (DSL).

Communication connection(s) 1150 refers to the hardware/softwareemployed to connect the network interface 1148 to the bus 1118. Whilecommunication connection 1150 is shown for illustrative clarity insidecomputer 1112, it can also be external to computer 1112. Thehardware/software necessary for connection to the network interface 1148includes, for exemplary purposes only, internal and externaltechnologies such as, modems including regular telephone grade modems,cable modems and DSL modems, ISDN adapters, and Ethernet cards.

FIG. 12 is a schematic block diagram of a sample-computing environment1200 with which the subject innovation can interact. The system 1200includes one or more client(s) 1210. The client(s) 1210 can be hardwareand/or software (e.g., threads, processes, computing devices). Thesystem 1200 also includes one or more server(s) 1220. Thus, system 1200can correspond to a two-tier client server model or a multi-tier model(e.g., client, middle tier server, data server), amongst other models.The server(s) 1220 can also be hardware and/or software (e.g., threads,processes, computing devices). The servers 1220 can house threads toperform transformations by employing the subject innovation, forexample. One possible communication between a client 1210 and a server1220 may be in the form of a data packet transmitted between two or morecomputer processes.

The system 1200 includes a communication framework 1230 that can beemployed to facilitate communications between the client(s) 1210 and theserver(s) 1220. The client(s) 1210 are operatively connected to one ormore client data store(s) 1240 that can be employed to store informationlocal to the client(s) 1210. Similarly, the server(s) 1220 areoperatively connected to one or more server data store(s) 1250 that canbe employed to store information local to the servers 1220.

What has been described above includes examples of the variousembodiments. It is, of course, not possible to describe everyconceivable combination of components or methodologies for purposes ofdescribing the embodiments, but one of ordinary skill in the art mayrecognize that many further combinations and permutations are possible.Accordingly, the detailed description is intended to embrace all suchalterations, modifications, and variations that fall within the spiritand scope of the appended claims.

In particular and in regard to the various functions performed by theabove described components, devices, circuits, systems and the like, theterms (including a reference to a “means”) used to describe suchcomponents are intended to correspond, unless otherwise indicated, toany component which performs the specified function of the describedcomponent (e.g., a functional equivalent), even though not structurallyequivalent to the disclosed structure, which performs the function inthe herein illustrated exemplary aspects of the embodiments. In thisregard, it will also be recognized that the embodiments includes asystem as well as a computer-readable medium having computer-executableinstructions for performing the acts and/or events of the variousmethods. In addition, while a particular feature may have been disclosedwith respect to only one of several implementations, such feature may becombined with one or more other features of the other implementations asmay be desired and advantageous for any given or particular application.

1. A system that distinguishes between overlapping state distributionsof a plurality of multi-cell memory devices, comprising: a calculationcomponent that performs a mathematical operation on two or more statedistributions of a plurality of multi-cell memory devices, and generatesa resulting distribution; and an analysis component that employs theresulting distribution in connection with distinguishing betweenoverlapping state distributions of the plurality of multi-cell memorydevices.